Crack Growth

Crack growth research focuses on accurately predicting crack initiation and propagation in various materials, aiming to improve structural reliability and design optimization. Current efforts leverage machine learning, particularly deep learning architectures like UNets and recurrent neural networks (RNNs), along with Gaussian processes, to model complex crack behavior from limited data, often integrating physics-based models for improved accuracy and efficiency. These advancements enable faster and more reliable predictions of crack growth, impacting fields like structural health monitoring, materials science, and predictive maintenance by facilitating improved design, optimized resource allocation, and enhanced safety assessments.